Predictive network analysis identifies JMJD6 and other potential key drivers in Alzheimer's disease.

Despite decades of genetic studies on late-onset Alzheimer's disease, the underlying molecular mechanisms remain unclear. To better comprehend its complex etiology, we use an integrative approach to build robust predictive (causal) network models using two large human multi-omics datasets. We delineate bulk-tissue gene expression into single cell-type gene expression and integrate clinical and pathologic traits, single nucleotide variation, and deconvoluted gene expression for the construction of cell type-specific predictive network models.

Predictive metabolic networks reveal sex- and APOE genotype-specific metabolic signatures and drivers for precision medicine in Alzheimer's disease.

Introduction: Late-onset Alzheimer's disease (LOAD) is a complex neurodegenerative disease characterized by multiple progressive stages, glucose metabolic dysregulation, Alzheimer's disease (AD) pathology, and inexorable cognitive decline. Discovery of metabolic profiles unique to sex, apolipoprotein E (APOE) genotype, and stage of disease progression could provide critical insights for personalized LOAD medicine.

Methods: Sex- and APOE-specific metabolic networks were constructed based on changes in 127 metabolites of 656 serum samples from the Alzheimer's Disease Neuroimaging Initiative cohort.

Freeform optics for variable extended depth of field imaging.

Imaging depth of field is shallow in applications with high magnification and high numerical aperture, such as microscopy, resulting in images with in- and out-of-focus regions. Therefore, methods to extend depth of field are of particular interest. Researchers have previously shown the advantages of using freeform components to extend depth of field, with each optical system requiring a specially designed phase plate.

Predictive network modeling in human induced pluripotent stem cells identifies key driver genes for insulin responsiveness.

Insulin resistance (IR) precedes the development of type 2 diabetes (T2D) and increases cardiovascular disease risk. Although genome wide association studies (GWAS) have uncovered new loci associated with T2D, their contribution to explain the mechanisms leading to decreased insulin sensitivity has been very limited. Thus, new approaches are necessary to explore the genetic architecture of insulin resistance.

An MLP neural network for ECG noise removal based on Kalman filter.

In this paper, application of Artificial Neural Network (ANN) for electrocardiogram (ECG) signal noise removal has been investigated. First, 100 number of ECG signals are selected from Physikalisch-Technische Bundesanstalt (PTB) database and Kalman filter is applied to remove their low pass noise. Then a suitable dataset based on denoised ECG signal is configured and used to a Multilayer Perceptron (MLP) neural network to be trained.